arXiv Machine Learning June 16, 2026 By Zhuodong Liu, Xiangyu Li, Zhihao Zhang

pFedUL: Layer-Aware Federated Unlearning for Personalized Federated Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 16304v1 Announce Type: new Abstract: Federated unlearning (FU) enables the removal of specific data contributions from federated learning (FL) models to comply with regulations such as the General Data Protection Regulation (GDPR).

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 22

Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning

arXiv:2508. 05157v2 Announce Type: replace Abstract: Federated Learning (FL) enables collaborative training across distributed clients without sharing raw data, offering strong privacy benefits.

By Thinh Nguyen, Le Huy Khiem, Van-Tuan Tran, Khoa D Doan, Nitesh V Chawla, Kok-Seng Wong

safety

arXiv Machine Learning

2d ago

Global Federated Learning Strategies for Building Efficient Personalized Models

arXiv:2608. 15107v1 Announce Type: new Abstract: Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously.

By Seongyoon Kim

reinforcement-learning fine-tuning efficiency safety

arXiv Machine Learning

Jun 15

FedSPC: Shared Parameter Correction for Personalized Federated Learning

arXiv:2606. 13748v1 Announce Type: new Abstract: Personalized federated learning (PFL) is one of the important approaches in federated learning for addressing statistical heterogeneity while enabling client-specific adaptation.

By Kannanthodath Induchoodan Ajay Menon, Christian Prehofer, Yunfei Xu, Toru Hirano

arXiv Machine Learning

Aug 13

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

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By Mirko Konstantin, Stefan Zachow, Anirban Mukhopadhyay

arXiv AI

Jun 10

From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning

arXiv:2606. 10595v1 Announce Type: cross Abstract: Federated Learning (FL) has emerged as a promising solution for data hunger in centralized learning.

By Huong Nguyen, Micka\"el Bettinelli, Amirhossein Ghaffari, Alexandre Benoit, Hong-Tri Nguyen, Susanna Pirttikangas, Lauri Lov\'en

safety

arXiv Machine Learning

Jun 30

Personalized Additive Modeling for Multi-level Federated Learning

arXiv:2405. 16472v2 Announce Type: replace Abstract: Contemporary AI faces the challenge of balancing generality with user-specific personalization.

By Shutong Chen, Guodong Long, Tianyi Zhou, Jie Ma, Jing Jiang, Chengqi Zhang

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Onboarding Without Forgetting: Hypernetwork Personalization with Data-Free Replay for Personalized Federated Learning

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FedSPC: Shared Parameter Correction for Personalized Federated Learning

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